diff --git a/examples/quantization_w8a8_fp8/whisper_example.py b/examples/quantization_w8a8_fp8/whisper_example.py
new file mode 100644
index 0000000000..df18b0d118
--- /dev/null
+++ b/examples/quantization_w8a8_fp8/whisper_example.py
@@ -0,0 +1,46 @@
+from datasets import load_dataset
+from transformers import AutoProcessor, WhisperForConditionalGeneration
+
+from llmcompressor.modifiers.quantization import QuantizationModifier
+from llmcompressor.transformers import oneshot
+
+MODEL_ID = "openai/whisper-large-v2"
+
+# Load model.
+model = WhisperForConditionalGeneration.from_pretrained(
+    MODEL_ID, device_map="auto", torch_dtype="auto"
+)
+model.config.forced_decoder_ids = None
+processor = AutoProcessor.from_pretrained(MODEL_ID)
+processor.tokenizer.set_prefix_tokens(language="en", task="transcribe")
+
+# Configure the quantization algorithm and scheme.
+# In this case, we:
+#   * quantize the weights to fp8 with per channel via ptq
+#   * quantize the activations to fp8 with dynamic per token
+recipe = QuantizationModifier(
+    targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"]
+)
+
+# Apply quantization.
+oneshot(model=model, recipe=recipe)
+
+# Confirm generations of the quantized model look sane.
+print("========== SAMPLE GENERATION ==============")
+ds = load_dataset(
+    "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation[:1]"
+)
+sample = ds[0]["audio"]
+input_features = processor(
+    sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt"
+).input_features
+input_features = input_features.to(model.device)
+predicted_ids = model.generate(input_features, language="en", forced_decoder_ids=None)
+print(processor.batch_decode(predicted_ids, skip_special_tokens=False)[0])
+# Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel
+print("==========================================")
+
+# Save to disk in compressed-tensors format.
+SAVE_DIR = MODEL_ID.split("/")[1] + "-FP8-Dynamic"
+model.save_pretrained(SAVE_DIR, save_compressed=True)
+processor.save_pretrained(SAVE_DIR)